Adaptive expectations are a backward-looking way of forming forecasts. People start with an old expectation, observe what actually happened, and then revise the next forecast part of the way toward the realized outcome.
The Standard Updating Rule
One common formula is:
\[ E_t = E_{t-1} + \theta (p_{t-1} - E_{t-1}), \qquad 0 < \theta \le 1 \]
Here E_t is the new expectation, E_{t-1} is last period’s expectation, p_{t-1} is the realized value last period, and \\theta controls how fast expectations adjust.
If \\theta is small, forecasts change slowly. If \\theta is large, forecasts chase recent outcomes more aggressively.
Why It Matters In Macroeconomics
Adaptive expectations can generate persistence. If inflation was high yesterday, people revise inflation expectations upward today, which can help keep wage demands and price-setting behavior elevated.
That is one reason older macroeconomic models used adaptive expectations to explain sluggish adjustment and inflation inertia.
Main Limitation
The weakness of adaptive expectations is that they react slowly when the regime changes. If a central bank credibly changes policy, a purely backward-looking rule may keep predicting the old pattern for too long.
That limitation helped motivate rational-expectations models, which emphasize that people use broader information than past forecast errors alone.